Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

2015 ◽  
Author(s):  
Mohammad B. Shadmand ◽  
Robert S. Balog ◽  
Haitham Abu Rub
Keyword(s):  
Cost Function ◽  
Matrix Converter ◽  
Model Predictive Controller ◽  
Weight Factors ◽  
Predictive Controller ◽  
Auto Tuning ◽  
The Cost

scholarly journals Eco-Driving Optimal Controller for Autonomy Tracking of Two-Wheel Electric Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Y. Bello ◽  
T. Azib ◽  
C. Larouci ◽  
M. Boukhnifer ◽  
N. Rizoug ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Consumption Rate ◽  
Driver Behavior ◽  
Optimal Controller ◽  
Model Predictive Controller ◽  
Speed Limits ◽  
Additional Information ◽  
Predictive Controller ◽  
The Cost ◽  
Energy Consumption Rate

The eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle, and traject information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated with a nonlinear unidimensional optimization. The combination of both algorithms allows increasing around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is able to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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